#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
全息分形处理器
实现宇宙全息分形信息处理
"""

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from typing import List, Tuple, Optional

class HolographicFractalProcessor(nn.Layer):
    """
    全息分形处理器
    模拟宇宙的全息分形信息结构
    """
    
    def __init__(self, 
                 base_dim: int = 1024,
                 fractal_layers: int = 8):
        """
        初始化全息分形处理器
        
        Args:
            base_dim: 基础维度
            fractal_layers: 分形层数
        """
        super().__init__()
        
        self.base_dim = base_dim
        self.fractal_layers = fractal_layers
        
        # 分形编码器：从宏观到微观
        self.fractal_encoders = nn.LayerList([
            FractalEncoder(
                input_dim=base_dim // (2**i),
                output_dim=base_dim // (2**(i+1))
            )
            for i in range(fractal_layers)
        ])
        
        # 分形解码器：从微观到宏观
        self.fractal_decoders = nn.LayerList([
            FractalDecoder(
                input_dim=base_dim // (2**(i+1)),
                output_dim=base_dim // (2**i)
            )
            for i in reversed(range(fractal_layers))
        ])
        
        # 全息关联矩阵
        self.holographic_matrix = self.create_parameter(
            shape=[base_dim // (2**fractal_layers), 
                   base_dim // (2**fractal_layers)],
            default_initializer=nn.initializer.Orthogonal()
        )
        
        # 分形相似性检测器
        self.similarity_detector = FractalSimilarityDetector()
    
    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        """
        全息分形处理流程
        
        Args:
            x: 输入状态
            
        Returns:
            处理后的状态
        """
        # 分形压缩：从宏观到微观
        encoded_states = []
        current = x
        
        for i, encoder in enumerate(self.fractal_encoders):
            current = encoder(current)
            encoded_states.append(current)
            
            # 记录分形特性
            self.similarity_detector.record_encoding(
                layer=i,
                state=current,
                compression_ratio=2.0  # 每层压缩一半
            )
        
        # 全息核心处理（在最微观层面）
        holographic_core = paddle.matmul(
            encoded_states[-1], 
            self.holographic_matrix
        )
        
        # 分形重建：从微观到宏观
        current = holographic_core
        for i, decoder in enumerate(self.fractal_decoders):
            current = decoder(current)
            
            # 检查重建质量
            original_state = encoded_states[-(i+1)]
            reconstruction_similarity = F.cosine_similarity(
                current, original_state
            ).mean().item()
            
            self.similarity_detector.record_decoding(
                layer=i,
                similarity=reconstruction_similarity
            )
        
        return current
    
    def get_fractal_report(self) -> dict:
        """获取分形处理报告"""
        return self.similarity_detector.get_report()

class FractalEncoder(nn.Layer):
    """分形编码器 - 模拟宇宙尺度缩小"""
    
    def __init__(self, input_dim: int, output_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        
        # 尺度压缩网络
        self.compression = nn.Sequential(
            nn.Linear(input_dim, output_dim * 2),
            nn.GELU(),
            nn.Linear(output_dim * 2, output_dim)
        )
        
        # 自相似性保持网络
        self.self_similarity = nn.Linear(output_dim, output_dim)
        
        # 信息保留门
        self.info_gate = nn.Linear(output_dim, output_dim)
    
    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        """分形编码"""
        # 尺度压缩
        compressed = self.compression(x)
        
        # 自相似性保持
        similarity = self.self_similarity(compressed)
        
        # 信息保留控制
        info_gate = F.sigmoid(self.info_gate(compressed))
        
        # 融合输出
        output = info_gate * compressed + (1 - info_gate) * similarity
        
        return F.normalize(output)

class FractalDecoder(nn.Layer):
    """分形解码器 - 模拟宇宙尺度放大"""
    
    def __init__(self, input_dim: int, output_dim: int):
        super().__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        
        # 尺度扩展网络
        self.expansion = nn.Sequential(
            nn.Linear(input_dim, output_dim * 2),
            nn.GELU(),
            nn.Linear(output_dim * 2, output_dim)
        )
        
        # 复杂性涌现网络
        self.emergence = nn.Linear(output_dim, output_dim)
        
        # 模式生成门
        self.pattern_gate = nn.Linear(output_dim, output_dim)
    
    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        """分形解码"""
        # 尺度扩展
        expanded = self.expansion(x)
        
        # 复杂性涌现
        emerged = self.emergence(expanded)
        
        # 模式生成控制
        pattern_gate = F.sigmoid(self.pattern_gate(expanded))
        
        # 融合输出
        output = pattern_gate * expanded + (1 - pattern_gate) * emerged
        
        return F.normalize(output)

class FractalSimilarityDetector:
    """分形相似性检测器"""
    
    def __init__(self):
        self.encoding_records = []
        self.decoding_records = []
        self.similarity_scores = []
    
    def record_encoding(self, layer: int, state: paddle.Tensor, 
                       compression_ratio: float):
        """记录编码过程"""
        record = {
            'layer': layer,
            'state_norm': paddle.norm(state).item(),
            'state_entropy': self.calculate_entropy(state),
            'compression_ratio': compression_ratio,
            'fractal_dimension': self.estimate_fractal_dimension(state)
        }
        self.encoding_records.append(record)
    
    def record_decoding(self, layer: int, similarity: float):
        """记录解码过程"""
        record = {
            'layer': layer,
            'reconstruction_similarity': similarity
        }
        self.decoding_records.append(record)
        self.similarity_scores.append(similarity)
    
    def calculate_entropy(self, state: paddle.Tensor) -> float:
        """计算状态熵值"""
        prob_dist = F.softmax(state, axis=-1)
        entropy = -paddle.sum(prob_dist * paddle.log(prob_dist + 1e-8))
        return entropy.item()
    
    def estimate_fractal_dimension(self, state: paddle.Tensor) -> float:
        """估计分形维度"""
        # 使用盒计数法估算分形维度
        norms = paddle.abs(state)
        scales = paddle.linspace(0.1, 1.0, 10)
        counts = []
        
        for scale in scales:
            threshold = scale * paddle.max(norms)
            count = paddle.sum(norms > threshold).float()
            counts.append(count)
        
        if len(counts) < 2:
            return 1.0
        
        # 线性拟合求分形维度
        log_scales = paddle.log(1/scales)
        log_counts = paddle.log(paddle.stack(counts))
        
        # 简单线性回归
        fractal_dim = (log_counts[-1] - log_counts[0]) / (log_scales[-1] - log_scales[0])
        return fractal_dim.item()
    
    def get_report(self) -> dict:
        """生成分形处理报告"""
        if not self.similarity_scores:
            return {}
        
        avg_similarity = np.mean(self.similarity_scores)
        min_similarity = np.min(self.similarity_scores)
        
        # 分形质量评估
        if avg_similarity > 0.9:
            fractal_quality = "优秀"
            quality_score = 0.95
        elif avg_similarity > 0.8:
            fractal_quality = "良好"
            quality_score = 0.8
        elif avg_similarity > 0.7:
            fractal_quality = "一般"
            quality_score = 0.6
        else:
            fractal_quality = "需要优化"
            quality_score = 0.4
        
        return {
            'fractal_quality': fractal_quality,
            'quality_score': quality_score,
            'average_similarity': avg_similarity,
            'minimum_similarity': min_similarity,
            'total_layers': len(self.encoding_records),
            'encoding_records': self.encoding_records,
            'decoding_records': self.decoding_records
        }

class MultiscaleFractalProcessor(nn.Layer):
    """
    多尺度分形处理器
    同时处理多个尺度的分形信息
    """
    
    def __init__(self, base_dim: int = 1024, num_scales: int = 5):
        super().__init__()
        self.base_dim = base_dim
        self.num_scales = num_scales
        
        # 多尺度分形网络
        self.multiscale_networks = nn.LayerList([
            HolographicFractalProcessor(
                base_dim=base_dim // (2**i),
                fractal_layers=4
            )
            for i in range(num_scales)
        ])
        
        # 尺度融合网络
        self.scale_fusion = nn.Linear(base_dim * num_scales, base_dim)
    
    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        """多尺度分形处理"""
        scale_outputs = []
        
        for i, network in enumerate(self.multiscale_networks):
            # 调整输入尺度
            if i > 0:
                # 下采样到对应尺度
                scale_factor = 2**i
                scaled_input = F.adaptive_avg_pool1d(
                    x.unsqueeze(1), 
                    x.shape[-1] // scale_factor
                ).squeeze(1)
            else:
                scaled_input = x
            
            # 分形处理
            scale_output = network(scaled_input)
            scale_outputs.append(scale_output)
        
        # 多尺度融合
        fused = paddle.concat(scale_outputs, axis=-1)
        output = self.scale_fusion(fused)
        
        return F.normalize(output)